{"id":8772,"date":"2025-12-02T07:02:26","date_gmt":"2025-12-02T07:02:26","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/02\/day-1-k-nn-regressor-in-excel-how-distance-drives-prediction\/"},"modified":"2025-12-02T07:02:26","modified_gmt":"2025-12-02T07:02:26","slug":"day-1-k-nn-regressor-in-excel-how-distance-drives-prediction","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/02\/day-1-k-nn-regressor-in-excel-how-distance-drives-prediction\/","title":{"rendered":"The Machine Learning \u201cAdvent Calendar\u201d Day 1: k-NN Regressor in Excel"},"content":{"rendered":"<p>    The Machine Learning \u201cAdvent Calendar\u201d Day 1: k-NN Regressor in Excel<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>This first day of the Advent Calendar introduces the k-NN regressor, the simplest distance-based model. Using Excel, we explore how predictions rely entirely on the closest observations, why feature scaling matters, and how heterogeneous variables can make distances meaningless. Through examples with continuous and categorical features, including the California Housing and Diamonds datasets, we see the strengths and limitations of k-NN, and why defining the right distance is essential to reflect real-world structure.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/day-1-k-nn-regressor-in-excel-how-distance-drives-prediction\/\">The Machine Learning \u201cAdvent Calendar\u201d Day 1: k-NN Regressor in Excel<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    angela shi<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/day-1-k-nn-regressor-in-excel-how-distance-drives-prediction\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Machine Learning \u201cAdvent Calendar\u201d Day 1: k-NN Regressor in Excel This first day of the Advent Calendar introduces the k-NN regressor, the simplest distance-based model. Using Excel, we explore how predictions rely entirely on the closest observations, why feature scaling matters, and how heterogeneous variables can make distances meaningless. Through examples with continuous and [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,2076,83,67,245,1770,70],"tags":[370,4336,4335],"class_list":["post-8772","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-algorithms","category-data-science","category-deep-dives","category-excel","category-k-nearest-neighbors","category-machine-learning","tag-advent","tag-calendar","tag-nn"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8772"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=8772"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8772\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=8772"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=8772"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=8772"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}